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6th International Conference on computing, Electronics and Communications Engineering, iCCECE 2023

作者： Yasmin, Israt Sultana, Suriya Begum, Syeda Jobaida Patwary, Muhammed J. A. Almohamad, Tarik Adnan Salam, Iftekhar International Islamic University Chittagong Department of Computer Science and Engineering Chattogram Bangladesh Karabuk University Electrical-Electronics Engineering Karabuk Turkey Xiamen University Malaysia School of Computing and Data Science Sepang Malaysia

ISBN: (纸本)9798350340921

Skin lesion is one of the most commonly encountered illnesses that need to be detected and treated at an early stage. Numerous Convolutional Neural Network (CNN) classifiers were utilized to identify skin conditions using visual features. When it comes to computational effectiveness and accuracy, the recently released Vision Transformers (ViT) have been shown to outperform CNNs. Since the ViTs perform well in classifying images, we use ViT models in our approach. This research proposes a fuzziness-based transformer model to classify images. The divide-And-conquer technique is employed to examine the effectiveness of fuzzy logic and classifying images more accurately. To increase the effectiveness of image classification, the testing dataset has been divided into three groups, including low, medium, and high-fuzzy instances, based on the degree of fuzziness for each sample. For better classification of images, we propose a straightforward mechanism that consists of a fuzzy divide-And-conquer strategy with ViT. Experiments are carried out to compare the model's performance with and without using this proposed mechanism. We noticed that retraining classifiers with low fuzzy samples can enhance image classifier performance. The classification performance on the PAD-UFES-20 dataset using this strategy revealed substantial improvements in the classifier's performance. © 2023 IEEE.

关键词： Image enhancement

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A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Model for Classifying DNA Barcodes 1

A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Mo...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Yadav, Abhivyakti Divya, S. Oviya, I.R. Raja, Kalpana Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Medicine Yale University Section for Biomedical Informatics and Data Science New HavenCT United States

ISBN: (纸本)9798350342796

DNA Barcodes, which are particular fragments derived from brief sections of DNA (such as mitochondrial, nuclear, and plastid sequences), can be used to identify organisms from the major life kingdoms. In addition to supporting conventional taxonomic techniques, DNA barcoding is a potent tool that advances our knowledge of species diversity and their ecological functions. On a variety of organisms, the use of this approach for species categorization has been successful. In this paper, we examine how DNA barcoding has been used to classify species based on DNA barcodes as well as other related research that has been done over the years on the subject. After experimenting with a number of deep learning models, we have propose a Variational Auto Encoder + Feed Forward Neural Network workflow for classifiying species using DNA barcodes. The models have been assessed on the basis of performance factors including accuracy, recall, and precision. COI, rbcL, matK, and ITS are the specific gene sections that have been identified as barcodes. For both simulated and real datasets, the model can attain an average accuracy of greater than 95 percent. This DNA barcoding approach has the ability to simplify DNA barcode-based species identification and serve as a tool for species categorization. © 2023 IEEE.

关键词： DNA

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PHIrecord: A Medical Record Management System for Rural Health Facilities in the Philippines 6

PHIrecord: A Medical Record Management System for Rural Heal...

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6th IEEE Symposium on Computers and Informatics, ISCI 2024

作者： Diaz, Allen G. Gumtang, Adrienne Denise Orpiada, Cristine Joy A. Balagot, Angelika S. Villanueva, Evelyn A. Manalang, Marycris A. Central Luzon State University Department of Information Technology Science City of Muñoz Philippines Central Luzon State University Mobile and Cloud Computing Lab Department of Information Technology Science City of Muñoz Philippines Central Luzon State University Data Science and Artificial Intelligence Research Lab Department of Information Technology Science City of Muñoz Philippines

ISBN: (纸本)9798350353853

Traditionally, doctors have relied on paper-based methods to manage their patients' medical histories and treatments. However, this approach has several issues, including inaccuracies, record misplacement, and difficulties in accessing records when needed. These problems can lead to a time-consuming and inefficient process that hinders the efficiency and effectiveness of medical care. This study proposed a medical record management system that provides a centralized location for storing and sharing patients' information. This system enables doctors and nurses to access patients' medical records quickly and easily, leading to more effective and efficient care. Additionally, it allows multiple health facilities to seamlessly access and share medical records, eliminating the need for patients to provide the same information multiple times. Furthermore, the system includes a patient referral feature, allowing patients to be referred to another health facility if their doctor is not available, while enabling the receiving health facility to access the referred patients' information. For patient information confidentiality, doctors can only view the information of patients that are assigned to them and write doctor prescriptions. Patients can also track their health, diagnosis, and prescriptions by viewing their own medical records through the system. Moreover, the system allows patients to request the official copy of their medical records from the health facility where they are registered. The proposed system introduces new methods and procedures to rural health facilities that will help improve the storing, sharing, and securing of patients' medical records. © 2024 IEEE.

关键词： Electronic health record

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FedEdge: Accelerating Edge-Assisted Federated Learning 23

FedEdge: Accelerating Edge-Assisted Federated Learning

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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Federated learning (FL) has been widely acknowledged as a promising solution to training machine learning (ML) model training with privacy preservation. To reduce the traffic overheads incurred by FL systems, edge servers have been included between clients and the parameter server to aggregate clients' local models. Recent studies on this edge-assisted hierarchical FL scheme have focused on ensuring or accelerating model convergence by coping with various factors, e.g., uncertain network conditions, unreliable clients, heterogeneous compute resources, etc. This paper presents our three new discoveries of the edge-assisted hierarchical FL scheme: 1) it wastes significant time during its two-phase training rounds;2) it does not recognize or utilize model diversity when producing a global model;and 3) it is vulnerable to model poisoning attacks. To overcome these drawbacks, we propose FedEdge, a novel edge-assisted hierarchical FL scheme that accelerates model training with asynchronous local federated training and adaptive model aggregation. Extensive experiments are conducted on two widely-used public datasets. The results demonstrate that, compared with state-of-the-art FL schemes, FedEdge accelerates model convergence by 1.14 × -3.20 ×, and improves model accuracy by 2.14% - 6.63%. © 2023 ACM.

关键词： Edge computing

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Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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Soft computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

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Meta Heuristic Image Optimisation in LiDAR Sensor Based on Cloud IoT Network and Deep Learning Algorithms

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Remote Sensing in Earth Systems sciences 2025年第2期8卷 566-575页

作者： Meenakshi, K. Manoj, Gunna Diwakar, Meenakshi Prabakaran, Manjula Sravan, Padala Babu, Gaddam Ravindra Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Cyber Security Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad India Department of IT Panimalar Engineering College Chennai India Dept of CSE (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh Angallu India Computer Science and Engineering NIIT with learning partner Hainan Normal University Haikou China Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India

LiDAR technology has become a vital tool for researching these atmospheric components because of its capacity to measure backscattered signals and investigate the atmosphere at extremely high spatial and temporal resolution. However, machine learning has become a potent technique for sophisticated data processing and analysis because of the complexity and diversity of lidar technology. The aim of this research is to propose a novel technique in cloud IoT network-based LiDAR sensor imaging with image optimization using a metaheuristic deep learning model. Input has been collected LiDAR image using a cloud IoT module and processed for noise removal and normalization. Then this processed image is classified and optimized utilizing a quantile transfer graph convolutional model with binary firefly whale colony optimization. The experimental analysis is carried out in terms of detection accuracy, random precision, recall, RMSE, and AUC for various LiDAR sensor-based datasets. Comparing the results of the various approaches reveals that the suggested approach outperforms them all with a recall of 94%, a random precision of 95%, and a detection accuracy of 98%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Cloud IoT network Convolutional model Image optimization LiDAR sensor Metaheuristic deep learning

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Analysing Several Hair and Noise Removal Techniques on a Skin Lesion during the Preprocessing Stage

Analysing Several Hair and Noise Removal Techniques on a Ski...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： S Praveena T. Veeramakali Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The cancer identified as melanoma is a highly serious and fast form of the disease, and it doesn’t have any of the typical symptoms of malignancy. Melanoma accounts for approximately seventy-five percent of all occurrences of skin cancer. In the scenario patients are given an accurate diagnosis of melanoma as soon as feasible, and have a greater probability of surviving the disease. The initial dermoscopy image preprocessing is a process that is both essential and challenging. However, the presence of hair is a common a barrier that arises during the dermoscopic examination of tiny skin lesions. among its most significant tasks of the preprocessing state involves the identification and removal of hair and other tiny particles. This paper presents a thorough analysis of several preprocessing approaches used for skin lesions. Particularly, it emphasizes on various methods for minimizing noise. The Dullrazor hair removal procedure and various morphological operators are explored in detail. Furthermore, it focuses on a wide variety of datasets and the fundamental aspects of preprocessing information. Ultimately, a thorough analysis of the different performance parameter values is provided.

关键词： Hair Deep learning Accuracy Noise Melanoma Skin Lesions

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On Fake News Detection with LLM Enhanced Semantics Mining

On Fake News Detection with LLM Enhanced Semantics Mining

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Ma, Xiaoxiao Zhang, Yuchen Ding, Kaize Yang, Jian Wu, Jia Fan, Hao School of Computing Macquarie University Sydney Australia Amazon Machine Learning Sydney Australia School of Information Management Wuhan University Hubei China Department of Statistics and Data Science Northwestern University IL United States

ISBN: (纸本)9798891761643

Large language models (LLMs) have emerged as valuable tools for enhancing textual features in various text-related tasks. Despite their superiority in capturing the lexical semantics between tokens for text analysis, our preliminary study on two popular LLMs, i.e., GPT-3.5 and Llama2, shows that simply applying news embeddings from LLMs is ineffective for fake news detection. Such embeddings only encapsulate the language styles between tokens. Meanwhile, the high-level semantics among named entities and topics, which reveal the deviating patterns of fake news, have been ignored. Therefore, we propose a topic model together with a set of specially designed prompts to extract topics and real entities from LLMs and model the relations among news, entities, and topics as a heterogeneous graph to facilitate investigating news semantics. We then propose a Generalized Page-Rank model and a consistent learning criterion for mining the local and global semantics centered on each news piece through the adaptive propagation of features across the graph. Our model shows superior performance on five benchmark datasets over seven baseline methods and the efficacy of the key ingredients has been thoroughly validated. © 2024 Association for Computational Linguistics.

关键词： Embeddings

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Deep Learning-Based Emotion Recognition using CNN, LSTM for Multimodal Text, Speech, and Facial Analysis

Deep Learning-Based Emotion Recognition using CNN, LSTM for ...

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data Intelligence and Cognitive Informatics (ICDICI), International Conference on

作者： Talat Binti Firdou Mohammed Ashra A. V. Kalpan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389616

Emotion detection is a critical field in AI as well as computer vision, seeking to recognize or detect human emotions from facial expressions. Although this area has been greatly advanced through Convolutional Neural Networks (CNNs), current methods using only single-modal data often exhibit reduced accuracy. In this work, we confront these challenges with a hybrid deep learning method for disparate modalities of information such as speech, video, and text. We pre-process and then use CNN feature extractors for the FER2013 data set. The hybrid deep learning model, hence proposed, achieved a weighted accuracy of 79.3% on FER2013, overcoming the conventional unimodal methods. We are using transformers library with the pre-trained RoBERTa model for text. For speech emotion features, we are using TESS dataset then using a CNN and LSTM (Long Short-Term Memory) model. The CNN extracts feature from the speech, while the LSTM is used for temporal analysis to detect emotions. Once these multimodal features have been obtained, our work would be to fuse in order to get the final emotion recognition using Deep Neural Networks (DNNs). The process increases the precision and reliability of the recognition systems, helping to accumulate even more detailed intuition for different emotional states for a combined approach.

关键词： Deep learning Emotion recognition Accuracy Virtual assistants Speech recognition Feature extraction Convolutional neural networks Reliability Speech processing Long short term memory

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Prior-guided longitudinal metabolomic analysis

Prior-guided longitudinal metabolomic analysis

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Sarker, Kowshika Zhu, Ruoqing Holscher, Hannah Diane Zhai, ChengXiang University of Illinois at Urbana-Champaign Siebel School of Computing and Data Science Champaign United States University of Illinois at Urbana-Champaign Department of Statistics Champaign United States University of Illinois at Urbana-Champaign Food Science and Human Nutrition Urbana United States

ISBN: (纸本)9798350386226

Longitudinal metabolomics frequently benefits biomarker discovery and other applications. Existing studies ignore prior knowledge of holistic metabolic systems, which can yield interpretable features and causal insights. We integrate longitudinal metabolome with prior knowledge from a genome-scale metabolic model and propose a graph recurrent neural network-based novel representation learning framework to infer interpretable heuristic features for metabolic reactions and metabolic subsystems. In downstream clustering and classification, our proposed features often outperform and sometimes match metabolome-based performances. Our novel features can also reveal new hypotheses on causal mechanisms, such as potential reactions and subsystems involved in underlying biomechanism. Novel causal understanding and improved downstream performances affirm the impact of domain knowledge in time-series metabolome analyses. © 2024 IEEE.

关键词： Contrastive Learning

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